U.S. patent application number 15/443283 was filed with the patent office on 2018-08-30 for automated generation of scheduling algorithms based on task relevance assessment.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to CARLOS HENRIQUE CARDONHA, RENATO LUIS de FREITAS CUNHA, VITOR HENRIQUE LEAL MESQUITA, EDUARDO ROCHA RODRIGUEZ.
Application Number | 20180246758 15/443283 |
Document ID | / |
Family ID | 63246314 |
Filed Date | 2018-08-30 |
United States Patent
Application |
20180246758 |
Kind Code |
A1 |
CARDONHA; CARLOS HENRIQUE ;
et al. |
August 30, 2018 |
AUTOMATED GENERATION OF SCHEDULING ALGORITHMS BASED ON TASK
RELEVANCE ASSESSMENT
Abstract
A method for automatically generating scheduling algorithms,
including determining a scheduling policy for a plurality of jobs
to be executed on a computer system, where the scheduling policy
specifies an execution order of a plurality of jobs; using the
scheduling policy in a production environment for a period of time,
and collecting data indicative of a business impact of each job
executed during the period of time; generating a list of all
pairwise comparisons of business impact between the plurality of
jobs, together with outcomes of the comparisons; marking each pair
for which the comparison outcome is inconsistent with the relative
execution order of the pair of jobs according to a predefined
criteria to create a reinforcement learning batch; and using the
reinforcement learning batch to adjust a decision criteria used to
determine the scheduling policy.
Inventors: |
CARDONHA; CARLOS HENRIQUE;
(Sao Paulo, BR) ; de FREITAS CUNHA; RENATO LUIS;
(Sao Paulo,, BR) ; LEAL MESQUITA; VITOR HENRIQUE;
(Sao Paulo, BR) ; ROCHA RODRIGUEZ; EDUARDO; (Sao
Paulo, BR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
63246314 |
Appl. No.: |
15/443283 |
Filed: |
February 27, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/0633 20130101;
G06N 20/00 20190101; G06F 9/4881 20130101; G06Q 10/0631 20130101;
G06Q 10/06312 20130101; G06N 7/005 20130101 |
International
Class: |
G06F 9/48 20060101
G06F009/48; G06N 99/00 20060101 G06N099/00 |
Claims
1. A method for automatically generating scheduling algorithms,
comprising the steps of: determining a scheduling policy for a
plurality of jobs to be executed on a computer system, wherein the
scheduling policy specifies an execution order of a plurality of
jobs; using the scheduling policy in a production environment for a
period of time, and collecting data indicative of a business impact
of each job executed during the period of time; generating a list
of all pairwise comparisons of business impact between the
plurality of jobs, together with outcomes of the comparisons;
marking each pair for which the comparison outcome is inconsistent
with the relative execution order of said pair of jobs according to
a predefined criteria to create a reinforcement learning batch; and
using the reinforcement learning batch to adjust a decision
criteria used to determine the scheduling policy.
2. The method of claim 1, wherein the marking of each pair is
performed manually.
3. The method of claim 1, wherein the marking of each pair is
performed automatically, by associating each job with a measurable
value, and marking each pair for which the measured value of a job
is inconsistent with the priority of that job.
4. The method of claim 3, wherein the measurable values include an
amount of revenue generated by the associated job, a public
relations impact of the associated job, an ecological impact of the
associated job.
5. A system for automatically generating a scheduling algorithm,
comprising: a scheduler that schedules jobs for a limited period of
time based on a predetermined decision criteria and compares
measurable values of pairs of jobs, wherein the measurable value is
are indicative of a business impact of the associated job; a
verification module that checks outcomes of some or all of the
pairwise comparisons made by the scheduler and indicates which were
wrong, based on whether the outcome is consistent with the
respective priorities of each job; and a reinforcement learning
algorithm that generates a new set of decision criteria to be used
by the scheduler.
6. The system of claim 5, wherein the measurable values include an
amount of revenue generated by the associated job, a public
relations impact of the associated job, an ecological impact of the
associated job.
7. A method for automatically generating a scheduling algorithm for
a computer system, comprising the steps of: defining a set of
features for each job of a plurality of jobs; defining a set of
priority classes into which the plurality of jobs are classified;
clustering a set of historic job data to define clusters of jobs
belonging to a same priority class, and identifying a
representative job for each cluster; defining a priority level for
each cluster; executing jobs on said computer system based on job
requests received from users; calculating an average distance
between a most recent number of jobs and the representative of each
respective cluster; determining whether said average distance
exceeds surpasses a pre-defined threshold, and defining a new set
of priority classes into which the plurality of jobs are
classified, when said average distance exceeds surpasses said
pre-defined threshold.
8. The method of claim 7, wherein the representative job for each
cluster is based on a centroid of each cluster.
9. The method of claim 7, wherein priorities are assigned to
clusters based on a business value of the jobs in the cluster,
wherein jobs in more valuable clusters receive a higher
priority.
10. The method of claim 7, wherein identifying a cluster to which
each job belongs comprises computing an Euclidean distance between
each job and the representative of each cluster and selecting the
cluster with a smallest value.
11. The method of claim 7, further comprising storing the distance
between an incoming job and the representative of its cluster.
12. The method of claim 7, wherein executing jobs on said computer
system comprises: receiving job requests; extracting features from
each received job; identifying a cluster to which each job belongs;
assigning a priority to the received job based on the cluster to
which said job belongs; submitting the job and its priority level
to a scheduler; and executing said job.
13. A non-transitory program storage device readable by a computer,
tangibly embodying a program of instructions executed by the
computer to perform the method steps for automatically generating
scheduling algorithms, comprising the steps of: determining a
scheduling policy for a plurality of jobs to be executed on a
computer system, wherein the scheduling policy specifies an
execution order of a plurality of jobs; using the scheduling policy
in a production environment for a period of time, and collecting
data indicative of a business impact of each job executed during
the period of time; generating a list of all pairwise comparisons
of business impact between the plurality of jobs, together with
outcomes of the comparisons; marking each pair for which the
comparison outcome is inconsistent with the relative execution
order of said pair of jobs according to a predefined criteria to
create a reinforcement learning batch; and using the reinforcement
learning batch to adjust a decision criteria used to determine the
scheduling policy.
14. The computer readable program storage device of claim 13,
wherein the marking of each pair is performed manually.
15. The computer readable program storage device of claim 13,
wherein the marking of each pair is performed automatically, by
associating each job with a measurable value, and marking each pair
for which the measured value of a job is inconsistent with the
priority of that job.
16. The computer readable program storage device of claim 15,
wherein the measurable values include an amount of revenue
generated by the associated job, a public relations impact of the
associated job, an ecological impact of the associated job.
17. A non-transitory program storage device readable by a computer,
tangibly embodying a program of instructions executed by the
computer to perform the method steps for automatically generating a
scheduling algorithm for a computer system, comprising the steps
of: defining a set of features for each job of a plurality of jobs;
defining a set of priority classes into which the plurality of jobs
are classified; clustering a set of historic job data to define
clusters of jobs belonging to a same priority class, and
identifying a representative job for each cluster; defining a
priority level for each cluster; executing jobs on said computer
system based on job requests received from users; calculating an
average distance between a most recent number of jobs and the
representative of each respective cluster; determining whether said
average distance exceeds surpasses a pre-defined threshold, and
defining a new set of priority classes into which the plurality of
jobs are classified, when said average distance exceeds surpasses
said pre-defined threshold.
18. The computer readable program storage device of claim 17,
wherein the representative job for each cluster is based on a
centroid of each cluster.
19. The computer readable program storage device of claim 17,
wherein priorities are assigned to clusters based on a business
value of the jobs in the cluster, wherein jobs in more valuable
clusters receive a higher priority.
20. The computer readable program storage device of claim 17,
wherein identifying a cluster to which each job belongs comprises
computing an Euclidean distance between each job and the
representative of each cluster and selecting the cluster with a
smallest value.
21. The computer readable program storage device of claim 17,
wherein the method further comprises storing the distance between
an incoming job and the representative of its cluster.
22. The computer readable program storage device of claim 17,
wherein executing jobs on said computer system comprises: receiving
job requests; extracting features from each received job;
identifying a cluster to which each job belongs; assigning a
priority to the received job based on the cluster to which said job
belongs; submitting the job and its priority level to a scheduler;
and executing said job.
Description
TECHNICAL FIELD
[0001] Embodiments of the present disclosure are directed to
self-adjusting scheduling algorithms.
DISCUSSION OF THE RELATED ART
[0002] A challenge faced by service providers is to establish good
scheduling policies to determine the order in which a list of tasks
should be executed in situations where current system capacity is
not able to avoid the formation of queues. Several scheduling
policies have been proposed in the literature already, most of
which are based on priority queues, in which tasks with higher
priority are solved first, where the execution order of tasks
reflect their relevance. In many situations, though, task relevance
is ill-posed, in the sense that even system administrators are not
aware of all the rules that should be applied for this assessment;
in these cases, trial-and-error methodologies are typically
employed, which is a manual process that is susceptible to errors
and which might take a very long time to converge. Furthermore,
these methodologies prioritize the minimization of cost while a
more appropriate metric should be maximization of impact or
expected return.
SUMMARY
[0003] Exemplary embodiments of the present disclosure are directed
to a system and method for the automatic adjustment of a scheduling
policy based on a verification module that checks whether previous
decisions on comparisons between jobs were correct or if the
outcome should be changed. This verification module may be manual,
e.g., by relying on manual user feedback, or automatic, e.g., by
basing the comparisons on the business impact of each job. Many
data centers periodically review project priorities and use these
priorities to set scheduling policies. A system and method
according to exemplary embodiments can be used by the review
committees to automatically adjust the scheduling priorities.
Businesses can use a system and method according to an embodiment
to determine which jobs produce the largest returns and prioritize
them. For example, a weather forecast center can automatically
prioritize certain simulations to address weather hazards.
[0004] According to an embodiment of the disclosure, there is
provided a method for automatically generating scheduling
algorithms, including determining a scheduling policy for a
plurality of jobs to be executed on a computer system, where the
scheduling policy specifies an execution order of a plurality of
jobs; using the scheduling policy in a production environment for a
period of time, and collecting data indicative of a business impact
of each job executed during the period of time; generating a list
of all pairwise comparisons of business impact between the
plurality of jobs, together with outcomes of the comparisons;
marking each pair for which the comparison outcome is inconsistent
with the relative execution order of the pair of jobs according to
a predefined criteria to create a reinforcement learning batch; and
using the reinforcement learning batch to adjust a decision
criteria used to determine the scheduling policy.
[0005] According to a further embodiment of the disclosure, the
marking of each pair is performed manually.
[0006] According to a further embodiment of the disclosure, the
marking of each pair is performed automatically, by associating
each job with a measurable value, and marking each pair for which
the measured value of a job is inconsistent with the priority of
that job.
[0007] According to a further embodiment of the disclosure, the
measurable values include an amount of revenue generated by the
associated job, a public relations impact of the associated job, an
ecological impact of the associated job.
[0008] According to an embodiment of the disclosure, there is
provided a system for automatically generating a scheduling
algorithm, including a scheduler that schedules jobs for a limited
period of time based on a predetermined decision criteria and
compares measurable values of pairs of jobs, where the measurable
value is are indicative of a business impact of the associated job;
a verification module that checks outcomes of some or all of the
pairwise comparisons made by the scheduler and indicates which were
wrong, based on whether the outcome is consistent with the
respective priorities of each job; and a reinforcement learning
algorithm that generates a new set of decision criteria to be used
by the scheduler.
[0009] According to a further embodiment of the disclosure, the
measurable values include an amount of revenue generated by the
associated job, a public relations impact of the associated job, an
ecological impact of the associated job.
[0010] According to another embodiment of the disclosure, there is
provided a method for automatically generating a scheduling
algorithm for a computer system, including defining a set of
features for each job of a plurality of jobs; defining a set of
priority classes into which the plurality of jobs are classified;
clustering a set of historic job data to define clusters of jobs
belonging to a same priority class, and identifying a
representative job for each cluster; defining a priority level for
each cluster; executing jobs on the computer system based on job
requests received from users; calculating an average distance
between a most recent number of jobs and the representative of each
respective cluster; determining whether the average distance
exceeds surpasses a pre-defined threshold, and defining a new set
of priority classes into which the plurality of jobs are
classified, when the average distance exceeds surpasses the
pre-defined threshold.
[0011] According to a further embodiment of the disclosure, the
representative job for each cluster is based on a centroid of each
cluster.
[0012] According to a further embodiment of the disclosure,
priorities are assigned to clusters based on a business value of
the jobs in the cluster, where jobs in more valuable clusters
receive a higher priority.
[0013] According to a further embodiment of the disclosure,
identifying a cluster to which each job belongs comprises computing
an Euclidean distance between each job and the representative of
each cluster and selecting the cluster with a smallest value.
[0014] According to a further embodiment of the disclosure, the
method including storing the distance between an incoming job and
the representative of its cluster.
[0015] According to a further embodiment of the disclosure,
executing jobs on the computer system further includes receiving
job requests; extracting features from each received job;
identifying a cluster to which each job belongs; assigning a
priority to the received job based on the cluster to which the job
belongs; submitting the job and its priority level to a scheduler;
and executing the job.
[0016] According to another embodiment of the disclosure, there is
provided a non-transitory program storage device readable by a
computer, tangibly embodying a program of instructions executed by
the computer to perform the method steps for automatically
generating scheduling algorithms.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1 depicts a system that implements a self-adjusting
scheduling algorithm according to embodiments of the
disclosure.
[0018] FIG. 2 is a flow chart of a self-adjusting scheduling
algorithm according to embodiments of the disclosure.
[0019] FIGS. 3A and 3B are flowcharts of a workflow according to an
embodiment of a large airplane manufacturing company.
[0020] FIG. 4 is a schematic of an exemplary cloud computing node
that implements an embodiment of the disclosure.
[0021] FIG. 5 shows an exemplary cloud computing environment
according to embodiments of the disclosure.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0022] Exemplary embodiments of the disclosure as described herein
generally include methods for automatically generating scheduling
algorithms based on task relevance. Accordingly, while the
disclosure is susceptible to various modifications and alternative
forms, specific embodiments thereof are shown by way of example in
the drawings and will herein be described in detail. It should be
understood, however, that there is no intent to limit the
disclosure to the particular forms disclosed, but on the contrary,
the disclosure is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the disclosure.
In addition, it is understood in advance that although this
disclosure includes a detailed description on cloud computing,
implementation of the teachings recited herein are not limited to a
cloud computing environment. Rather, embodiments of the present
invention are capable of being implemented in conjunction with any
other type of computing environment now known or later
developed.
[0023] Exemplary embodiments of the present disclosure are directed
to a system and method that learns a scheduling policy based on
automated or user-guided verification of the relative ordering of
tasks being currently scheduled by a scheduler. Exemplary
embodiments of the present disclosure can identify suitable
scheduling policies based on local verifications that can be
performed a posteriori automatically or in a human-guided way. FIG.
1 depicts a system that implements a self-adjusting scheduling
algorithm according to embodiments of the disclosure. Referring now
to the figure, a system according to an embodiment includes a
scheduler 11 that employs certain decision criteria to schedule
jobs for a limited period of time, such as one day; a verification
module 12 that checks the outcomes of all, or a subset of, pairwise
comparisons made by the scheduler and indicates which were wrong;
and a reinforcement learning algorithm 13 that generates a new set
of decision criteria to be used by the scheduler 11.
[0024] FIG. 2 is a flow chart of a self-adjusting scheduling
algorithm according to embodiments of the disclosure. Referring now
to the figure, a method for self-adjusted scheduling begins at step
21 by determining a scheduling policy, such as a first-in-first-out
(FIFO) scheduling policy or a last-in-first-out (LIFO). At step 22,
the scheduling policy is used in a production environment for a
certain period of time, such as one day, one week, or one month.
For each job executed during this time period, an indicator value
indicative of the business impact of each job is determined. At
step 23, the scheduler compares the business impact indicator
values for each pair of executed jobs, and generates a list of
these comparisons together with the outcome of these comparisons,
e.g., which is greater than, equal to, or less than the other. In
addition, the scheduler compares the priorities for each pair of
executed jobs, and generates a list of these comparisons together
with the outcome of these comparisons. Each pair for which a
decision made by the scheduling policy, based on the relative
priorities of the pair of executed jobs, is inconsistent with the
comparison outcome according to certain criteria is marked by the
verification module at step 24, creating a reinforcement learning
batch with negative rewards. The other pairs, which were correct,
are submitted as well, but with positive rewards. In some
embodiments, at step 24a, the comparisons are human-guided, in
which service provider systems administrators verify each
comparison made by the scheduling policy, or a subset of the list,
and manually mark those which were wrong. In other embodiments, the
comparisons are performed automatically. In these embodiments, each
job is associated at step 24b1 with a certain value a posteriori,
indicating its business impact, which can be measured, e.g.,
according to the amount of revenue the associated process
generated, the public relations impact, the ecological impact, etc.
The list of pairs is visited at step 24b2, and results of the
comparisons are checked against the associated business impacts.
For example, if jobs j1 and j2 were compared, where j had a higher
priority, and j2 head a larger business impact, then pair (j1, j2)
with outcome j1 is included in the reinforcement learning batch as
an example of a wrong decision. Otherwise, it is included as an
example of a correct decision. At step 25, a reinforcement learning
algorithm according to an embodiment uses a reinforcement learning
batch previously obtained to adjust the decision criteria used by
the scheduler to determine the scheduling policy. The adjusted
policy can be used again at step 22.
[0025] According to embodiments of the disclosure, reinforcement
learning methods rely on tabular methods. One such method is
Q-Learning, which can find an optimal action-selection policy for
any finite Markov decision process by learning an action-value
function that gives the expected utility of taking a given action
in a given state and following the optimal policy thereafter. A
policy is a rule that the agent follows in selecting actions, given
the state it is in. The Q-learning equation is
Q ( s t , a t ) .rarw. Q ( s t , a t ) old value + .alpha. t
learning rate ( r t + 1 reward + .gamma. discount factor max
.alpha. Q ( s t + 1 , .alpha. ) estimate of optimal future value
learned value - Q ( s t , a t ) old value ) ##EQU00001##
where is the reward observed after performing action in state, and
where .alpha. is the learning rate, where 0<.alpha..ltoreq.1,
.gamma. is a discount factor that trades off the importance of
sooner versus later rewards and can be interpreted as the
likelihood to succeed at every time step, and Q is initialized to a
predetermined value.
[0026] The value of scheduling a pair of jobs relative to each
other, contained in variable s.sub.t above, is the previous value
plus the newly learned reward weighted by a "learning rate"
parameter. The learning rate determines whether more weight should
be given to previously learned values or to the newly-received
reward. For example, when .alpha. is 0.5, the mean of the reward
and the previously learned value are used. After building a batch
with both positive rewards, for correctly-ordered pairs of jobs,
and negative rewards, for wrong decisions, the batch is submitted
to a learning algorithm, which will update its Q-values. The
Q-values here are the algorithm's estimate of the reward of
scheduling one of the jobs first, so that the parameter a, is going
to be either one of the jobs. As time passes, the algorithm learns
with its mistakes and determines how to order the jobs. As for the
states that go into s.sub.t, embodiments can use a simple majority
rule, in which cluster A is prioritized over cluster B if most of
A's jobs have a higher business impact than those belonging to
B.
[0027] According to embodiments of the disclosure, consider a large
company that performs several R&D activities in several
different divisions. For example, a large airplane manufacturing
company may have several divisions: one each for wings, engines,
structural simulation, and accounting. This company has a shared
multi-processor supercomputer, and the system administrator needs
to determine the priority of jobs, which might change over time.
FIGS. 3A and 3B are flowcharts of a workflow according to an
embodiment of a large airplane manufacturing company.
[0028] Referring to FIG. 3A, a training phase according to an
embodiment begins at step 311 by defining set of features for each
job submitted to the supercomputer. Examples of features include,
but are not limited to: project, division(s), submission time,
expected execution time, resource consumption, user ID, etc.
[0029] At step 312, the system administrator defines the number of
priority classes that will be used by the system. For example, two
jobs belonging to a same priority class are ordered according to
their submission time, whereas a job with higher priority is always
serviced first.
[0030] At step 313, a clustering algorithm according to an
embodiment, which is an unsupervised learning algorithm, is applied
to a set of historic job data to define clusters/groups of jobs
belonging to the same priority class. In addition, a clustering
algorithm according to an embodiment can also identify a
representative job for each cluster, based on, e.g., the centroid
of the cluster.
[0031] At step 314, to define the priority level of each clusters,
there are at least two possible scenarios: (a) the system
administrator provides a business impact evaluation function/table
that assigns a business value to the priority class, and based on
these values, a system according to an embodiment automatically
assigns priorities to clusters, where more valuable clusters
receive a higher priority; or (b) the system administrator receives
pairs of representatives of each class and indicates manually which
should be serviced first. It is at step 314 that a method for
self-adjusted scheduling such as that illustrated by FIG. 2 would
be executed to compute the priority levels of each job.
[0032] Referring now to FIG. 3B, in a scheduling phase according to
an embodiment, job requests submitted by users from all divisions
are received at step 321. Features are extracted from the jobs at
step 322. At step 323, the cluster to which the job belongs is
identified, by, e.g., computing an Euclidean distance between each
job and the representative of each cluster and selecting the
cluster with smallest value. At step 324, the distance between an
incoming job and the representative of its cluster is stored, and a
corresponding priority is assigned to the incoming job at step 325.
At step 326, the job, together with its priority level, are
submitted to the scheduler.
[0033] A readjustment phase according to an embodiment verifies, at
step 331, whether the average distance between the last n incoming
jobs and the representative elements of their clusters exceeds a
pre-defined threshold. If so, at step 332, a workflow returns to
step 312 of a training phase according to an embodiment.
System Implementations
[0034] It is to be understood that embodiments of the present
disclosure can be implemented in various forms of hardware,
software, firmware, special purpose processes, or a combination
thereof. In one embodiment, an embodiment of the present disclosure
can be implemented in software as an application program tangible
embodied on a computer readable program storage device. The
application program can be uploaded to, and executed by, a machine
comprising any suitable architecture. Furthermore, it is understood
in advance that although this disclosure includes a detailed
description on cloud computing, implementation of the teachings
recited herein are not limited to a cloud computing environment.
Rather, embodiments of the present disclosure are capable of being
implemented in conjunction with any other type of computing
environment now known or later developed. An automatic
troubleshooting system according to an embodiment of the disclosure
is also suitable for a cloud implementation.
[0035] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0036] Characteristics are as follows:
[0037] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0038] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0039] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0040] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0041] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0042] Service Models are as follows:
[0043] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based email). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0044] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0045] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0046] Deployment Models are as follows:
[0047] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0048] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0049] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0050] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for loadbalancing between
clouds).
[0051] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0052] Referring now to FIG. 4, a schematic of an example of a
cloud computing node is shown. Cloud computing node 410 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the disclosure described herein. Regardless, cloud
computing node 410 is capable of being implemented and/or
performing any of the functionality set forth herein above.
[0053] In cloud computing node 410 there is a computer
system/server 412, which is operational with numerous other general
purpose or special purpose computing system environments or
configurations. Examples of well-known computing systems,
environments, and/or configurations that may be suitable for use
with computer system/server 412 include, but are not limited to,
personal computer systems, server computer systems, thin clients,
thick clients, handheld or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0054] Computer system/server 412 may be described in the general
context of computer system executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server
412 may be practiced in distributed cloud computing environments
where tasks are performed by remote processing devices that are
linked through a communications network. In a distributed cloud
computing environment, program modules may be located in both local
and remote computer system storage media including memory storage
devices.
[0055] As shown in FIG. 4, computer system/server 412 in cloud
computing node 410 is shown in the form of a general-purpose
computing device. The components of computer system/server 412 may
include, but are not limited to, one or more processors or
processing units 416, a system memory 428, and a bus 418 that
couples various system components including system memory 428 to
processor 416.
[0056] Bus 418 represents one or more of any of several types of
bus structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component Interconnect
(PCI) bus.
[0057] Computer system/server 412 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 412, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0058] System memory 428 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
430 and/or cache memory 432. Computer system/server 412 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 434 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 418 by one or more data
media interfaces. As will be further depicted and described below,
memory 428 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the disclosure.
[0059] Program/utility 440, having a set (at least one) of program
modules 442, may be stored in memory 428 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 442
generally carry out the functions and/or methodologies of
embodiments of the disclosure as described herein.
[0060] Computer system/server 412 may also communicate with one or
more external devices 414 such as a keyboard, a pointing device, a
display 424, etc.; one or more devices that enable a user to
interact with computer system/server 412; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 412
to communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 422.
Still yet, computer system/server 412 can communicate with one or
more networks such as a local area network (LAN), a general wide
area network (WAN), and/or a public network (e.g., the Internet)
via network adapter 420. As depicted, network adapter 420
communicates with the other components of computer system/server
412 via bus 418. It should be understood that although not shown,
other hardware and/or software components could be used in
conjunction with computer system/server 412. Examples, include, but
are not limited to: microcode, device drivers, redundant processing
units, external disk drive arrays, RAID systems, tape drives, and
data archival storage systems, etc.
[0061] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 400 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 400 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 5 are intended to be illustrative only and that computing
nodes 900 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0062] While embodiments of the present disclosure has been
described in detail with reference to exemplary embodiments, those
skilled in the art will appreciate that various modifications and
substitutions can be made thereto without departing from the spirit
and scope of the disclosure as set forth in the appended
claims.
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